Results 131 to 140 of about 207,453 (329)

In Situ Polymerized Composite Electrolytes for High‐Performance Solid‐State Lithium Batteries: A Review

Advanced Science, EarlyView.
This review systematically explores the recent advances in in situ polymerized composite polymer electrolytes (CPEs) for solid‐state lithium batteries. It covers the fundamentals of reaction mechanisms, monomer chemistry, and their impact on interfacial stability, ionic conductivity, and electrochemical performance.
Jialin Li, Yufen Yan, Xiaoju Wang, Yuhui Huang, Zhen Huang, Lingjie Zhang, Ningzhong Bao, Zijian Hong, Yongjun Wu, Dali Ji   +9 more
wiley   +1 more source

Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia. [PDF]

Sci Rep, 2022
Ladyzynski P, Molik M, Foltynski P.
europepmc   +1 more source

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes. [PDF]

Comput Methods Programs Biomed, 2022
Longato E, Morieri ML, Sparacino G, Di Camillo B, Cattelan A, Lo Menzo S, Trevenzoli M, Vianello A, Guarnieri G, Lionello F, Avogaro A, Fioretto P, Vettor R, Fadini GP.   +13 more
europepmc   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Roadmap for High‐Throughput Ceramic Materials Synthesis and Discovery for Batteries

Advanced Energy Materials, EarlyView.
This work examines ceramic synthesis through the lens of high‐throughput synthesis and optimization, identifying opportunities for faster, adaptable routes. It emphasizes flexible liquid precursor–to–solid film methods over slower solid‐state approaches and highlights computer‐aided decision making to optimize both material properties and device ...
Jesse J. Hinricher, Katarzyna P. Sokol, Philipp Simons, Kun Joong Kim, Michael Foshey, Yunsheng Tian, Thorben Prein, Lincoln J. Miara, Elsa Olivetti, Wojciech Matusik, Jennifer L. M. Rupp   +10 more
wiley   +1 more source

Dynamic Bayesian Network Learning to Infer Sparse Models From Time Series Gene Expression Data [PDF]

, 2021
Hamda Ajmal, Michael G. Madden
openalex   +1 more source

The Extended Parameter Filter

, 2013
The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step.
Erol, Yusuf, Li, Lei, Ramsundar, Bharath, Russell, Stuart J.   +3 more
core  

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases

, 2022
Marco Molinari, Andrea Cremaschi, Maria De Iorio, Nish Chaturvedi, Alun D. Hughes, Therese Tillin   +5 more
openalex   +1 more source